Indexed regular expression search with N-grams

ABSTRACT

A query directed at a source table organized into a set of batch units is received. The query comprises a regular expression search pattern. The regular expression search pattern is converted to a pruning index predicate comprising a set of substring literals extracted from the regular expression search pattern. A set of N-grams is generated based on the set of substring literals extracted from the regular expression search pattern. A pruning index associated with the source table is accessed. The pruning index indexes distinct N-grams in each column of the source table. A subset of batch units to scan for data matching the query are identified based on the pruning index and the set of N-grams. The query is processed by scanning the subset of batch units.

PRIORITY CLAIM

This application is a Continuation-in-part of U.S. patent applicationSer. No. 17/649,642, entitled “PRUNING USING PREFIX INDEXING,” filed onFeb. 1, 2022, now issued as U.S. Pat. No. 11,487,763, which is aContinuation of U.S. Pat. No. 11,275,739, entitled “PREFIX INDEXING,”filed Sep. 27, 2021, which is a Continuation of U.S. Pat. No.11,275,738, entitled “PREFIX N-GRAM INDEXING,” filed Sep. 24, 2021,which claims priority to U.S. Provisional Patent Application No.63/260,874 filed on Sep. 3, 2021 and is a Continuation-in-part of U.S.Pat. No. 11,321,325, entitled “PRUNING INDEX GENERATION FOR PATTERNMATCHING QUERIES,” filed on Jul. 29, 2021, which is a Continuation ofU.S. Pat. No. 11,113,286, entitled “GENERATION OF PRUNING INDEX FORPATTERN MATCHING QUERIES”, filed Mar. 31, 2021, which is a Continuationof U.S. Pat. No. 10,997,179, entitled “PRUNING INDEX FOR OPTIMIZATION OFPATTERN MATCHING QUERIES”, filed Oct. 30, 2020, which claims priority toU.S. Provisional Patent Application No. 63/084,394 filed on Sep. 28,2020 and is a continuation-in-part of U.S. Pat. No. 10,942,925, entitled“DATABASE QUERY PROCESSING USING A PRUNING INDEX,” filed on Jul. 17,2020, which is a continuation of U.S. Pat. No. 10,769,150, entitled“PRUNING INDEXES TO ENHANCE DATABASE QUERY PROCESSING,” filed on Dec.26, 2019, the contents of which are incorporated herein by reference intheir entireties.

TECHNICAL FIELD

Embodiments of the disclosure relate generally to databases and, morespecifically, to using a pruning index in processing queries thatinclude regular expressions.

BACKGROUND

When certain information is to be extracted from a database, a querystatement may be executed against the database data. A database systemprocesses the query and returns certain data according to one or morequery predicates that indicate what information should be returned bythe query. The database system extracts specific data from the databaseand formats that data into a readable form. However, it can bechallenging to execute queries on a very large table because asignificant amount of time and computing resources are required to scanan entire table to identify data that satisfies the query.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will be understood more fully from the detaileddescription given below and from the accompanying drawings of variousembodiments of the disclosure.

FIG. 1 illustrates an example computing environment that includes anetwork-based database system in communication with a cloud storageprovider system, in accordance with some embodiments of the presentdisclosure.

FIG. 2 is a block diagram illustrating components of a compute servicemanager, in accordance with some embodiments of the present disclosure.

FIG. 3 is a block diagram illustrating components of an executionplatform, in accordance with some embodiments of the present disclosure.

FIG. 4 is a conceptual diagram illustrating generation of an exampleblocked bloom filter, which may form part of a pruning index, inaccordance with some example embodiments.

FIG. 5 illustrates a portion of an example pruning index, in accordancewith some embodiments of the present disclosure.

FIG. 6 is a conceptual diagram illustrating further details regardingthe creation of an example pruning index, in accordance with someembodiments.

FIG. 7 is a conceptual diagram illustrating maintenance of a pruningindex, in accordance with some embodiments.

FIGS. 8-14 are flow diagrams illustrating operations of thenetwork-based database system in performing a method for generating andusing a pruning index in processing a database query, in accordance withsome embodiments of the present disclosure.

FIGS. 15A-15D are conceptual diagrams illustrating example optimizationsperformed on a pruning index predicate in the form of an expressiontree, in accordance with some embodiments.

FIG. 16 is a conceptual diagram illustrating additional exampleoptimizations performed on a pruning index predicate in the form of anexpression tree, in accordance with some embodiments.

FIG. 17 illustrates a diagrammatic representation of a machine in theform of a computer system within which a set of instructions may beexecuted for causing the machine to perform any one or more of themethodologies discussed herein, in accordance with some embodiments ofthe present disclosure.

DETAILED DESCRIPTION

Reference will now be made in detail to specific example embodiments forcarrying out the inventive subject matter. Examples of these specificembodiments are illustrated in the accompanying drawings, and specificdetails are set forth in the following description to provide a thoroughunderstanding of the subject matter. It will be understood that theseexamples are not intended to limit the scope of the claims to theillustrated embodiments. On the contrary, they are intended to coversuch alternatives, modifications, and equivalents as may be includedwithin the scope of the disclosure.

As noted above, processing queries directed to very large tables ischallenging because a significant amount of time and computing resourcesare required to scan an entire table to identify data that satisfies thequery. Therefore, it can be desirable to execute a query withoutscanning the entire table. Aspects of the present disclosure address theabove and other challenges in processing queries on large tables bycreating a pruning index that may be used to construct a reduced scanset for processing a query. More specifically, a large source table maybe organized into a set of batch units such as micro-partitions, and apruning index can be created for the source table to be used inidentifying a subset of the batch units to scan to identify data thatsatisfies the query.

As discussed herein, a “micro-partition” is a batch unit, and eachmicro-partition has contiguous units of storage. By way of example, eachmicro-partition may contain between 50 MB and 500 MB of uncompresseddata (note that the actual size in storage may be smaller because datamay be stored compressed). Groups of rows in tables may be mapped intoindividual micro-partitions organized in a columnar fashion. This sizeand structure allow for extremely granular selection of themicro-partitions to be scanned, which can be comprised of millions, oreven hundreds of millions, of micro-partitions. This granular selectionprocess for micro-partitions to be scanned is referred to herein as“pruning.” Pruning involves using metadata to determine which portionsof a table, including which micro-partitions or micro-partitiongroupings in the table, are not pertinent to a query, avoiding thosenon-pertinent micro-partitions when responding to the query, andscanning only the pertinent micro-partitions to respond to the query.Metadata may be automatically gathered about all rows stored in amicro-partition, including: the range of values for each of the columnsin the micro-partition; the number of distinct values; and/or additionalproperties used for both optimization and efficient query processing. Inone embodiment, micro-partitioning may be automatically performed on alltables. For example, tables may be transparently partitioned using theordering that occurs when the data is inserted/loaded. However, itshould be appreciated that this disclosure of the micro-partition isexemplary only and should be considered non-limiting. It should beappreciated that the micro-partition may include other database storagedevices without departing from the scope of the disclosure.

Consistent with some embodiments, a network-based database systemgenerates a pruning index for a source table and uses the pruning indexto prune micro-partitions of the source table when processing queriesdirected to the source table. In generating a pruning index, thenetwork-based database system generates a filter for eachmicro-partition of the source table that indexes distinct values (ordistinct N-grams) in each column of the micro-partition of the sourcetable. The filter may, for example, comprise a blocked bloom filter, abloom filter, a hash filter, or a cuckoo filter.

In general, the pruning index includes a probabilistic data structurethat stores fingerprints (e.g., bit patterns) for all searchable valuesin a source table. The fingerprints are based on hashes computed basedon searchable values in the source table. In some embodiments, thefingerprints are based on a hash computed based on N-grams ofpreprocessed variants of each searchable value in the source table.

Fingerprints are computed for all N-grams that are generated for eachsearchable value. For a given value, the database system generates a setof N-grams by breaking the value into multiple segments of N-length. Inan example, the value of N is three and the searchable value is“solution.” In this example, the database system computes fingerprintsfor “sol,” “olu,” “lut,” “uti,” “tio,” and “ion.” Depending on theembodiment, a single value for N may be used in generating the set ofN-grams, or multiple values of N can be used. That is, the N-grams inthe set of N-grams may be the same size or there may be multiple sizesof N-grams in the set.

Generating the pruning index using multiple values of N can be costly interms of index lookups and storage. To address the cost issue related touse of multiple values of N, the database system uses an approach thatutilizes a prefix property that exists between N-grams of differentsizes that start at the same offset in the indexed text. In the previousexample of the searchable value of “solution,” the database systemconstructs the fingerprints for “solut” (e.g., where N=5) and “soluti”(e.g., where N=6) in such a way that the fingerprint of “soluti”contains a superset of the bits in the fingerprint of its prefix“solute.” This approach, where the fingerprint of the larger N-grams arebased on the fingerprint of the smaller N-grams that are prefixessignificantly reduces indexing and storage costs. This approach alsoprovides the benefit of providing a form of prefix compression for thefilters in the pruning index.

In a more specific example of prefix N-gram indexing, the searchablevalue is “testvalue” and N-grams are generated using N=5, 6, 7, and 8.More specifically, for: N=5, a first N-gram “testy” is generated; N=6, asecond N-gram “testva” is generated; N=7, a third N-gram “testva” isgenerated; and N=8, a fourth N-gram “testvalu” is generated. With N-gramindexing, the database system uses a constructive approach that movesfrom smaller to larger values of N by using the prefix property. Withspecific reference to the example, the database system starts with thesmallest value of N (N=5) and computes an initial hash based on theN-gram generated for that value (e.g., hash5=compute_hash(“testv”, 0)).The database system uses the initial hash to produce an initialfingerprint that is used to populate a filter in the pruning index. Theinitial hash can also be used to determine a particular filter in thepruning index to be populated with all N-grams that share the “testy”prefix. In embodiments that rely on a blocked bloom filter scheme forgenerating the pruning index, the initial hash is also used to determinea block to be populated with all N-grams that share the “testy” prefix.

In generating the fingerprint for the second N-gram in the set where N=6(“testva”), the database system computes a hash over the newly addedcharacters. That is, the database system computes a hash over a portionof the second N-gram that excludes the first N-gram. In computing thehash, the database system uses the initial fingerprint generated for thefirst N-gram as a seed to the hashing function (e.g., hash6=compute_hash(“a”, hash5). Seeding with the hash from the previous step provides theprefix property, expressing that the new character(s) were preceded byall that was hashed in previous steps.

The second fingerprint is used to populate the same filter (or morespecifically, the bloom filter block) as the initial fingerprint. Asnoted above, the filter (or more specifically, the bloom filter block)is determined by the fingerprint generated from the smallest N-gram,thereby maintaining low lookup costs for the pruning index given thatregardless of the value of N, the same filter (or block) can be scanned.

The database system continues the process set forth above, adding morebits to the same filter (or block) for all remaining values of N fromthe same offset that are to be indexed. The number of bits added at eachstep can be fixed or based on the value of N. For example, the number ofbits added at each step can be gradually decreased as the value of Nincreases (e.g., 6 bits for N=5, 4 bits for N=6, 2 bits for N=7, and 1bit for N=8). After generating a fingerprint for each of the abovereferenced N-grams, the database system then moves to the next offset inthe indexed value to start the process for the next set of N-grams untilthe input is exhausted.

It shall be appreciated that the prefix indexing approach used by thedatabase system is not limited to consecutive N-gram sizes. The prefixindexing approach can be applied for indexing hierarchical relationshipsin general, for example to index the hierarchy into a blocked bloomfilter or other hash-based data structure. As an example, the approachcan be extended such that an initial fingerprint is generated for anitem in the highest level in the hierarchy (for N-grams, this is thebase prefix). The hash of this root item determines (i) the filter (orspecific filter block) into which all fingerprints for items that arechildren to the root item go and (ii) the initial fingerprint to use topopulate the filter. Child items of this root on all the consecutivelevels in the hierarchy contribute additional fingerprint-bits(typically less bits the lower they are in the hierarchy) into theselected block. The hash that determines the bits for each item is basedon the hash of the direct root item as a seed for the hashing function.This establishes a connection between the hashes through their seeds andthe connection propagates from root to leaf and encodes a representationof the hierarchy into the filter.

As an example of the foregoing approach to indexing hierarchicalrelationships, assume that there are two streams of prefix n-gramindexing, one that captures N=5, 6, 7, 8, and another one that considersonly non-overlapping 8-grams. The second stream can use the final 8-gramhash from the first stream to capture the prefix relationship withneighboring, non-overlapping 8-grams. In essence, this would beequivalent to indexing N=5, 6, 7, 8, 16.

For a given query, the pruning index can be used to quickly disqualifymicro-partitions that are certain to not include data that satisfies thequery. When a query is received, rather than scanning the entire sourcetable to identify matching data, the network-based database systemprobes the pruning index to identify a reduced scan set ofmicro-partitions comprising only a subset of the micro-partitions of thesource table, and only the reduced scan set of micro-partitions isscanned when executing the query.

The database system can use a pruning index to prune a scan set forqueries with equality predicates (e.g., “=”) as well as queries withpattern matching predicates (e.g., LIKE, ILIKE, CONTAINS, STARTSWITH,ENDSWITH, etc.) and queries with regular expression predicates (e.g.,RLIKE, REGEXP, and REGEXP_LIKE). For a given equality predicate, thedatabase system uses the pruning index to identify a subset ofmicro-partitions to scan for data that completely matches an entirestring or other searchable value. For a given pattern matchingpredicate, the database system uses the pruning index to identify a setof micro-partitions to scan for data that matches a specified searchpattern, which can include one or more partial strings and one or morewildcards (e.g., “%” or “_”) used to represent wildcard characterpositions in the pattern (e.g., character positions whose underlyingvalue is unconstrained by the query).

To provide pruning index support to queries with regular expressionpredicates, the database system converts a regular expression searchpattern to one or more pruning index predicates used to query thepruning index. Each of the pruning index predicates corresponds to asubstring literal extracted from the regular expression search pattern(e.g., a portion of a string included in the regular expression searchpattern). For some embodiments, the one or more pruning index predicatescorrespond to a Boolean expression of substring literals that must matcha subject text from a source table. For some embodiments, the Booleanexpression is represented as an expression tree, which is a tree datastructure of pruning index predicates, each of which representssubstring literals from the regular expression search pattern. Thedatabase system decomposes the substring literals into N-grams and usesthe N-grams to search against the pruning index to prune partitions fromthe scan set. For some embodiments, prior to decomposing the substringliterals into N-grams, the database system may perform one or moreenhancements on the expression tree to optimize pruning functionalityprovided by the pruning index.

In an example, a query with the following regular expression searchpattern is received:

.*(str1){2,}(str2|str3|str4)(str5)+[a-z]*(str6)?.*

Based on this search pattern, two consecutive occurrences of str1 mustappear in the text, followed by either str2, str3, or str4, followed byat least one occurrence of str5. The database system can convert theregular expression search pattern to the following Boolean expression ofsubstring literals:

str1str1 AND (str2 OR str3 OR str4) AND str5

Any text that matches the original regular expression also needs tomatch the Boolean expression above.

As noted above, prior to using the Boolean expression of substringliterals to query the pruning index, the database system may applyoptimizations that cause longer strings to appear in a partition andhence maximize the number of N-grams used for filtering. Following theexample from above, the database system can modify the Booleanexpression as follows:

str1str1 AND (str1str2str5 OR str1str3str5 OR str1str4str5)

In this example, the database system concatenates part of the substringssurrounding the OR predicate with the strings inside the expression. Thesubstrings in the resulting expression are split into n-grams. Forexample, if the maximum length of n-grams in the pruning indexconfiguration is 8, the substrings above result in the following8-grams:

-   -   str1str1→{str1str1}    -   str1str2str5→{str1str2,tr1str2s,r1str2st,1str2str,str2str5}    -   str1str3str5→{str1str3,tr1str3s,r1str3st,1str3str,str3str5}    -   str1str4str5→{str1str4,tr1str4s,r1str4st,1str4str,str4str5}        The resulting N-grams are then searched against the pruning        index while respecting the Boolean expression, and non-matching        partitions identified using the pruning index are filtered out        of the scan set.

By using a pruning index to prune the set of micro-partitions to scan inexecuting a query, the database system accelerates the execution ofpoint queries on large tables when compared to conventionalmethodologies. Using a pruning index in this manner also guarantees aconstant overhead in search time for every searchable value on thetable. Additional benefits of pruning index utilization include, but arenot limited to, an ability to support multiple predicate types, anability to quickly compute the number of distinct values in a table, andthe ability to support join pruning.

Pruning index support for regular expression search, specifically,enables pruning of table partitions that cannot match the regularexpression pattern, potentially achieving significant speedups comparedto the conventional approaches that require a full table scan to run apossibly expensive regular expression match against a subject text inall rows of a table. The advantages are significant when we are able toextract long substring literals from the regular expression pattern thatmust be present in any matching subject text.

FIG. 1 illustrates an example computing environment 100 that includes adatabase system 102 in communication with a storage platform 104, inaccordance with some embodiments of the present disclosure. To avoidobscuring the inventive subject matter with unnecessary detail, variousfunctional components that are not germane to conveying an understandingof the inventive subject matter have been omitted from FIG. 1 . However,a skilled artisan will readily recognize that various additionalfunctional components may be included as part of the computingenvironment 100 to facilitate additional functionality that is notspecifically described herein.

As shown, the computing environment 100 comprises the database system102 and a storage platform 104 (e.g., AWS®, Microsoft Azure BlobStorage®, or Google Cloud Storage®). The database system 102 is used forreporting and analysis of integrated data from one or more disparatesources including storage devices 106-1 to 106-N within the storageplatform 104. The storage platform 104 comprises a plurality ofcomputing machines and provides on-demand computer system resources suchas data storage and computing power to the database system 102.

The database system 102 comprises a compute service manager 108, anexecution platform 110, and a database 114. The database system 102hosts and provides data reporting and analysis services to multipleclient accounts. Administrative users can create and manage identities(e.g., users, roles, and groups) and use permissions to allow or denyaccess to the identities to resources and services.

The compute service manager 108 coordinates and manages operations ofthe database system 102. The compute service manager 108 also performsquery optimization and compilation as well as managing clusters ofcomputing services that provide compute resources (also referred to as“virtual warehouses”). The compute service manager 108 can support anynumber of client accounts such as end users providing data storage andretrieval requests, system administrators managing the systems andmethods described herein, and other components/devices that interactwith compute service manager 108.

The compute service manager 108 is also in communication with a userdevice 112. The user device 112 corresponds to a user of one of themultiple client accounts supported by the database system 102. In someembodiments, the compute service manager 108 does not receive any directcommunications from the user device 112 and only receives communicationsconcerning jobs from a queue within the database system 102.

The compute service manager 108 is also coupled to database 114, whichis associated with the data stored in the computing environment 100. Thedatabase 114 stores data pertaining to various functions and aspectsassociated with the database system 102 and its users. In someembodiments, the database 114 includes a summary of data stored inremote data storage systems as well as data available from a localcache. Additionally, the database 114 may include information regardinghow data is organized in remote data storage systems (e.g., the storageplatform 104) and the local caches. The database 114 allows systems andservices to determine whether a piece of data needs to be accessedwithout loading or accessing the actual data from a storage device.

For example, the database 114 can include one or more pruning indexes.The compute service manager 108 may generate a pruning index for eachsource table accessed from the storage platform 104 and use a pruningindex to prune the set of micro-partitions of a source table to scan fordata in executing a query. That is, given a query directed at a sourcetable organized into a set of micro-partitions, the computing servicemanager 108 can access a pruning index from the database 114 and use thepruning index to identify a reduced set of micro-partitions to scan inexecuting the query. The set of micro-partitions to scan in executing aquery may be referred to herein as a “scan set.”

In some embodiments, the compute service manager 108 may determine thata job should be performed based on data from the database 114. In suchembodiments, the compute service manager 108 may scan the data anddetermine that a job should be performed to improve data organization ordatabase performance. For example, the compute service manager 108 maydetermine that a new version of a source table has been generated andthe pruning index has not been refreshed to reflect the new version ofthe source table. The database 114 may include a transactional changetracking stream indicating when the new version of the source table wasgenerated and when the pruning index was last refreshed. Based on thattransaction stream, the compute service manager 108 may determine that ajob should be performed. In some embodiments, the compute servicemanager 108 determines that a job should be performed based on a triggerevent and stores the job in a queue until the compute service manager108 is ready to schedule and manage the execution of the job. In anembodiment of the disclosure, the compute service manager 108 determineswhether a table or pruning index needs to be reclustered based on one ormore DML commands being performed, wherein one or more of the DMLcommands constitute the trigger event.

The compute service manager 108 is further coupled to the executionplatform 110, which provides multiple computing resources that executevarious data storage and data retrieval tasks. The execution platform110 is coupled to storage platform 104 of the storage platform 104. Thestorage platform 104 comprises multiple data storage devices 106-1 to106-N. In some embodiments, the data storage devices 106-1 to 106-N arecloud-based storage devices located in one or more geographic locations.For example, the data storage devices 106-1 to 106-N may be part of apublic cloud infrastructure or a private cloud infrastructure. The datastorage devices 106-1 to 106-N may be hard disk drives (HDDs), solidstate drives (SSDs), storage clusters, Amazon S3™ storage systems or anyother data storage technology. Additionally, the storage platform 104may include distributed file systems (e.g., Hadoop Distributed FileSystems (HDFS)), object storage systems, and the like.

The execution platform 110 comprises a plurality of compute nodes. A setof processes on a compute node executes a query plan compiled by thecompute service manager 108. The set of processes can include: a firstprocess to execute the query plan; a second process to monitor anddelete micro-partition files using a least recently used (LRU) policyand implement an out of memory (OOM) error mitigation process; a thirdprocess that extracts health information from process logs and status tosend back to the compute service manager 108; a fourth process toestablish communication with the compute service manager 108 after asystem boot; and a fifth process to handle all communication with acompute cluster for a given job provided by the compute service manager108 and to communicate information back to the compute service manager108 and other compute nodes of the execution platform 110.

In some embodiments, communication links between elements of thecomputing environment 100 are implemented via one or more datacommunication networks. These data communication networks may utilizeany communication protocol and any type of communication medium. In someembodiments, the data communication networks are a combination of two ormore data communication networks (or sub-networks) coupled to oneanother. In alternate embodiments, these communication links areimplemented using any type of communication medium and any communicationprotocol.

As shown in FIG. 1 , the data storage devices 106-1 to 106-N aredecoupled from the computing resources associated with the executionplatform 110. This architecture supports dynamic changes to the databasesystem 102 based on the changing data storage/retrieval needs as well asthe changing needs of the users and systems. The support of dynamicchanges allows the database system 102 to scale quickly in response tochanging demands on the systems and components within the databasesystem 102. The decoupling of the computing resources from the datastorage devices supports the storage of large amounts of data withoutrequiring a corresponding large amount of computing resources.Similarly, this decoupling of resources supports a significant increasein the computing resources utilized at a particular time withoutrequiring a corresponding increase in the available data storageresources.

The compute service manager 108, database 114, execution platform 110,and storage platform 104 are shown in FIG. 1 as individual discretecomponents. However, each of the compute service manager 108, database114, execution platform 110, and storage platform 104 may be implementedas a distributed system (e.g., distributed across multiplesystems/platforms at multiple geographic locations). Additionally, eachof the compute service manager 108, database 114, execution platform110, and storage platform 104 can be scaled up or down (independently ofone another) depending on changes to the requests received and thechanging needs of the database system 102. Thus, in the describedembodiments, the database system 102 is dynamic and supports regularchanges to meet the current data processing needs.

During typical operation, the database system 102 processes multiplejobs determined by the compute service manager 108. These jobs arescheduled and managed by the compute service manager 108 to determinewhen and how to execute the job. For example, the compute servicemanager 108 may divide the job into multiple discrete tasks and maydetermine what data is needed to execute each of the multiple discretetasks. The compute service manager 108 may assign each of the multiplediscrete tasks to one or more nodes of the execution platform 110 toprocess the task. The compute service manager 108 may determine whatdata is needed to process a task and further determine which nodeswithin the execution platform 110 are best suited to process the task.Some nodes may have already cached the data needed to process the taskand, therefore, be a good candidate for processing the task. Metadatastored in the database 114 assists the compute service manager 108 indetermining which nodes in the execution platform 110 have alreadycached at least a portion of the data needed to process the task. One ormore nodes in the execution platform 110 process the task using datacached by the nodes and, if necessary, data retrieved from the storageplatform 104. It is desirable to retrieve as much data as possible fromcaches within the execution platform 110 because the retrieval speed istypically much faster than retrieving data from the storage platform104.

As shown in FIG. 1 , the computing environment 100 separates theexecution platform 110 from the storage platform 104. In thisarrangement, the processing resources and cache resources in theexecution platform 110 operate independently of the data storage devices106-1 to 106-N in the storage platform 104. Thus, the computingresources and cache resources are not restricted to specific datastorage devices 106-1 to 106-N. Instead, all computing resources and allcache resources may retrieve data from, and store data to, any of thedata storage resources in the storage platform 104.

FIG. 2 is a block diagram illustrating components of the compute servicemanager 108, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 2 , the compute service manager 108includes an access manager 202 and a key manager 204 coupled to a datastorage device 206. Access manager 202 handles authentication andauthorization tasks for the systems described herein. Key manager 204manages storage and authentication of keys used during authenticationand authorization tasks. For example, access manager 202 and key manager204 manage the keys used to access data stored in remote storage devices(e.g., data storage devices in storage platform 104). As used herein,the remote storage devices may also be referred to as “persistentstorage devices” or “shared storage devices.”

A request processing service 208 manages received data storage requestsand data retrieval requests (e.g., jobs to be performed on databasedata). For example, the request processing service 208 may determine thedata necessary to process a received query (e.g., a data storage requestor data retrieval request). The data may be stored in a cache within theexecution platform 110 or in a data storage device in storage platform104.

A management console service 210 supports access to various systems andprocesses by administrators and other system managers. Additionally, themanagement console service 210 may receive a request to execute a joband monitor the workload on the system.

The compute service manager 108 also includes a job compiler 212, a joboptimizer 214, and a job executor 216. The job compiler 212 parses a jobinto multiple discrete tasks and generates the execution code for eachof the multiple discrete tasks. The job optimizer 214 determines thebest method to execute the multiple discrete tasks based on the datathat needs to be processed. The job optimizer 214 also handles variousdata pruning operations and other data optimization techniques toimprove the speed and efficiency of executing the job. The job executor216 executes the execution code for jobs received from a queue ordetermined by the compute service manager 108.

A job scheduler and coordinator 218 sends received jobs to theappropriate services or systems for compilation, optimization, anddispatch to the execution platform 110. For example, jobs may beprioritized and processed in that prioritized order. In an embodiment,the job scheduler and coordinator 218 determines a priority for internaljobs that are scheduled by the compute service manager 108 with other“outside” jobs such as user queries that may be scheduled by othersystems in the database but may utilize the same processing resources inthe execution platform 110. In some embodiments, the job scheduler andcoordinator 218 identifies or assigns particular nodes in the executionplatform 110 to process particular tasks. A virtual warehouse manager220 manages the operation of multiple virtual warehouses implemented inthe execution platform 110. As discussed below, each virtual warehouseincludes multiple execution nodes that each include a cache and aprocessor.

Additionally, the compute service manager 108 includes a configurationand metadata manager 222, which manages the information related to thedata stored in the remote data storage devices and in the local caches(e.g., the caches in execution platform 110). The configuration andmetadata manager 222 uses the metadata to determine which datamicro-partitions need to be accessed to retrieve data for processing aparticular task or job. A monitor and workload analyzer 224 overseesprocesses performed by the compute service manager 108 and manages thedistribution of tasks (e.g., workload) across the virtual warehouses andexecution nodes in the execution platform 110. The monitor and workloadanalyzer 224 also redistributes tasks, as needed, based on changingworkloads throughout the database system 102 and may furtherredistribute tasks based on a user (e.g., “external”) query workloadthat may also be processed by the execution platform 110. Theconfiguration and metadata manager 222 and the monitor and workloadanalyzer 224 are coupled to a data storage device 226. Data storagedevice 226 in FIG. 2 represents any data storage device within thedatabase system 102. For example, data storage device 226 may representcaches in execution platform 110, storage devices in storage platform104, or any other storage device.

As shown, the compute service manager 108 further includes a pruningindex generator 228. The pruning index generator 228 is responsible forgenerating pruning indexes to be used in pruning scan sets for queriesdirected to tables stored in the storage platform 104. Each pruningindex comprises a set of filters (e.g., blocked bloom filters, bloomfilters, hash filter, or cuckoo filters) that encode an existence ofunique N-grams in each column of a source table. The pruning indexgenerator 228 generates a filter for each micro-partition of a sourcetable and each filter indicates whether data matching a query ispotentially stored on a particular micro-partition of the source table.Further details regarding the generation of pruning indexes arediscussed below.

FIG. 3 is a block diagram illustrating components of the executionplatform 110, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 3 , the execution platform 110 includesmultiple virtual warehouses, including virtual warehouse 1, virtualwarehouse 2, and virtual warehouse N. Each virtual warehouse includesmultiple execution nodes that each includes a data cache and aprocessor. The virtual warehouses can execute multiple tasks in parallelby using the multiple execution nodes. As discussed herein, theexecution platform 110 can add new virtual warehouses and drop existingvirtual warehouses in real-time based on the current processing needs ofthe systems and users. This flexibility allows the execution platform110 to quickly deploy large amounts of computing resources when neededwithout being forced to continue paying for those computing resourceswhen they are no longer needed. All virtual warehouses can access datafrom any data storage device (e.g., any storage device in storageplatform 104).

Although each virtual warehouse shown in FIG. 3 includes three executionnodes, a particular virtual warehouse may include any number ofexecution nodes. Further, the number of execution nodes in a virtualwarehouse is dynamic, such that new execution nodes are created whenadditional demand is present, and existing execution nodes are deletedwhen they are no longer necessary.

Each virtual warehouse is capable of accessing any of the data storagedevices 106-1 to 106-N shown in FIG. 1 . Thus, the virtual warehousesare not necessarily assigned to a specific data storage device 106-1 to106-N and, instead, can access data from any of the data storage devices106-1 to 106-N within the storage platform 104. Similarly, each of theexecution nodes shown in FIG. 3 can access data from any of the datastorage devices 106-1 to 106-N. In some embodiments, a particularvirtual warehouse or a particular execution node may be temporarilyassigned to a specific data storage device, but the virtual warehouse orexecution node may later access data from any other data storage device.

In the example of FIG. 3 , virtual warehouse 1 includes three executionnodes 302-1, 302-2, and 302-N. Execution node 302-1 includes a cache304-1 and a processor 306-1. Execution node 302-2 includes a cache 304-2and a processor 306-2. Execution node 302-N includes a cache 304-N and aprocessor 306-N. Each execution node 302-1, 302-2, and 302-N isassociated with processing one or more data storage and/or dataretrieval tasks. For example, a virtual warehouse may handle datastorage and data retrieval tasks associated with an internal service,such as a clustering service, a materialized view refresh service, afile compaction service, a storage procedure service, or a file upgradeservice. In other implementations, a particular virtual warehouse mayhandle data storage and data retrieval tasks associated with aparticular data storage system or a particular category of data.

Similar to virtual warehouse 1 discussed above, virtual warehouse 2includes three execution nodes 312-1, 312-2, and 312-N. Execution node312-1 includes a cache 314-1 and a processor 316-1. Execution node 312-2includes a cache 314-2 and a processor 316-2. Execution node 312-Nincludes a cache 314-N and a processor 316-N. Additionally, virtualwarehouse 3 includes three execution nodes 322-1, 322-2, and 322-N.Execution node 322-1 includes a cache 324-1 and a processor 326-1.Execution node 322-2 includes a cache 324-2 and a processor 326-2.Execution node 322-N includes a cache 324-N and a processor 326-N.

In some embodiments, the execution nodes shown in FIG. 3 are statelesswith respect to the data the execution nodes are caching. For example,these execution nodes do not store or otherwise maintain stateinformation about the execution node or the data being cached by aparticular execution node. Thus, in the event of an execution nodefailure, the failed node can be transparently replaced by another node.Since there is no state information associated with the failed executionnode, the new (replacement) execution node can easily replace the failednode without concern for recreating a particular state.

Although the execution nodes shown in FIG. 3 each includes one datacache and one processor, alternate embodiments may include executionnodes containing any number of processors and any number of caches.Additionally, the caches may vary in size among the different executionnodes. The caches shown in FIG. 3 store, in the local execution node,data that was retrieved from one or more data storage devices in storageplatform 104. Thus, the caches reduce or eliminate the bottleneckproblems occurring in platforms that consistently retrieve data fromremote storage systems. Instead of repeatedly accessing data from theremote storage devices, the systems and methods described herein accessdata from the caches in the execution nodes, which is significantlyfaster and avoids the bottleneck problem discussed above. In someembodiments, the caches are implemented using high-speed memory devicesthat provide fast access to the cached data. Each cache can store datafrom any of the storage devices in the storage platform 104.

Further, the cache resources and computing resources may vary betweendifferent execution nodes. For example, one execution node may containsignificant computing resources and minimal cache resources, making theexecution node useful for tasks that require significant computingresources. Another execution node may contain significant cacheresources and minimal computing resources, making this execution nodeuseful for tasks that require caching of large amounts of data. Yetanother execution node may contain cache resources providing fasterinput-output operations, useful for tasks that require fast scanning oflarge amounts of data. In some embodiments, the cache resources andcomputing resources associated with a particular execution node aredetermined when the execution node is created, based on the expectedtasks to be performed by the execution node.

Additionally, the cache resources and computing resources associatedwith a particular execution node may change over time based on changingtasks performed by the execution node. For example, an execution nodemay be assigned more processing resources if the tasks performed by theexecution node become more processor intensive. Similarly, an executionnode may be assigned more cache resources if the tasks performed by theexecution node require a larger cache capacity.

Although virtual warehouses 1, 2, and N are associated with the sameexecution platform 110, the virtual warehouses may be implemented usingmultiple computing systems at multiple geographic locations. Forexample, virtual warehouse 1 can be implemented by a computing system ata first geographic location, while virtual warehouses 2 and N areimplemented by another computing system at a second geographic location.In some embodiments, these different computing systems are cloud-basedcomputing systems maintained by one or more different entities.

Additionally, each virtual warehouse is shown in FIG. 3 as havingmultiple execution nodes. The multiple execution nodes associated witheach virtual warehouse may be implemented using multiple computingsystems at multiple geographic locations. For example, an instance ofvirtual warehouse 1 implements execution nodes 302-1 and 302-2 on onecomputing platform at a geographic location and implements executionnode 302-N at a different computing platform at another geographiclocation. Selecting particular computing systems to implement anexecution node may depend on various factors, such as the level ofresources needed for a particular execution node (e.g., processingresource requirements and cache requirements), the resources availableat particular computing systems, communication capabilities of networkswithin a geographic location or between geographic locations, and whichcomputing systems are already implementing other execution nodes in thevirtual warehouse.

Execution platform 110 is also fault tolerant. For example, if onevirtual warehouse fails, that virtual warehouse is quickly replaced witha different virtual warehouse at a different geographic location.

A particular execution platform 110 may include any number of virtualwarehouses. Additionally, the number of virtual warehouses in aparticular execution platform is dynamic, such that new virtualwarehouses are created when additional processing and/or cachingresources are needed. Similarly, existing virtual warehouses may bedeleted when the resources associated with the virtual warehouse are nolonger necessary.

In some embodiments, the virtual warehouses may operate on the same datain storage platform 104, but each virtual warehouse has its ownexecution nodes with independent processing and caching resources. Thisconfiguration allows requests on different virtual warehouses to beprocessed independently and with no interference between the requests.This independent processing, combined with the ability to dynamicallyadd and remove virtual warehouses, supports the addition of newprocessing capacity for new users without impacting the performanceobserved by the existing users.

FIG. 4 is a conceptual diagram illustrating generation of a filter 400,which forms part of a pruning index generated by the database system 102based on a source table 402, in accordance with some exampleembodiments. As shown, the source table 402 is organized into multiplemicro-partitions and each micro-partition comprises multiple columns inwhich values are stored.

In generating a pruning index, the compute service manager 108 generatesa filter for each micro-partition of the source table 402, an example ofwhich is illustrated in FIG. 4 as blocked bloom filter 400. Blockedbloom filter 400 comprises multiple bloom filters and encodes theexistence of distinct N-grams present in each column of thecorresponding micro-partition. When a query is received, rather thanscanning the entire source table 402 to evaluate query, the databasesystem 102 probes the pruning index to identify a reduced scan set ofmicro-partitions comprising only a subset of the micro-partitions of thesource table 402.

As shown, the blocked bloom filter 400 is decomposed into N bloomfilters stored as individual columns of the pruning index to leveragecolumnar scans. In generating the blocked bloom filter 400 for aparticular micro-partition of the source table 402, N-grams of storedvalues or preprocessed variants thereof are transformed into bitpositions in the bloom filters. For example, a set of fingerprints(e.g., hash values) can be generated from N-grams of stored values ineach column of the micro-partition and the set of fingerprints may beused to set bits in the bloom filters. Each line of the blocked bloomfilter 400 is encoded and stored as a single row in the pruning index.Each bloom filter 400 is represented in the pruning index as atwo-dimensional array indexed by the fingerprints of the N-grams of thestored column values.

FIG. 5 illustrates a portion of an example pruning index 500, inaccordance with some embodiments of the present disclosure. The examplepruning index 500 is organized into a plurality of rows and columns. Thecolumns of the pruning index 500 comprise a partition number 502 tostore a partition identifier and a blocked bloom filter 504 (e.g., theblocked bloom filter 400) that is decomposed into multiple numericcolumns. Each column in the blocked bloom filter 504 represents a bloomfilter. To avoid obscuring the inventive subject matter with unnecessarydetail, various additional columns that are not germane to conveying anunderstanding of the inventive subject matter may have been omitted fromthe example pruning index 500 in FIG. 5 .

FIG. 6 is a conceptual diagram illustrating creation of an examplepruning index, in accordance with some embodiments. The creation of afilter (e.g., a blocked bloom filter) is performed by a specializedoperator within the compute service manager 108 that computes the set ofrows of the pruning index. This operator obtains all the columns of aparticular micro-partition of a source table and populates the filterfor that micro-partition.

If the total number of distinct N-grams in the source table is unknown,the compute service manager 108 allocates a maximum number of levels tothe pruning index, populates each filter, and then applies aconsolidation phase to merge the different filters in a finalrepresentation of the pruning index. The memory allocated to computethis information per micro-partition is constant. In the exampleillustrated in FIG. 6 , the memory allocated to compute this informationis a two-dimensional array of unsigned integers. The first dimension isindexed by the level (maximum number of levels) and the second dimensionis indexed by the number of bloom filters. Since each partition isprocessed by a single thread, the total memory is bounded by the numberof threads (e.g., 8) and the maximum level of levels.

As shown in FIG. 6 , at each partition boundary, the compute servicemanager 108 combines blocks based on a target bloom filter density. Forexample, the compute service manager 108 may combine blocks such thatthe bloom filter density is no more than half. Since the domain offingerprints (e.g., hashed values) is uniform, this can be doneincrementally or globally based on the observed number of distinctvalues computed above.

If the number of distinct values is known, the compute service manager108 determines the number of levels for the pruning index by dividingthe maximum number of distinct N-grams by the number of distinct N-gramsper level. To combine two levels, the compute service manager 108performs a logical OR on all the integers representing the filter.

For performance reasons, the filter functions (create and check) cancombine two hash functions (e.g., two 32-bit hash functions). Both thehash function computation and the filter derivation need to be identicalon both the execution platform 110 and compute service manager 108 toallow for pruning in compute service manager 108 and in the scan setinitialization in the execution platform 110.

FIG. 7 is a conceptual diagram illustrating maintenance of a pruningindex based on changes to a source table, in accordance with someembodiments. As shown, at 700, a change is made to a source table (e.g.,addition of one or more rows or columns). The change to the source tabletriggers generation of additional rows in the pruning index for eachchanged or new micro-partition of the source table, at 702. At a regularinterval, the newly produced rows in the pruning index are reclustered,at 704.

The compute service manager 108 uses a deterministic selection algorithmas part of clustering the prune index. The processing of eachmicro-partition in the source table creates a bounded (and mostlyconstant) number of rows based on the number of distinct N-grams in thesource micro-partition. By construction, those rows are known to beunique, and the index domain is non-overlapping for that partition andfully overlapping with already clustered index rows. To minimize thecost of clustering, the compute service manager 108 delays reclusteringof rows until a threshold number of rows has been produced to createconstant partitions.

Although the pruning index is described in some embodiments as beingimplemented specifically with blocked bloom filters, it shall beappreciated that the pruning index is not limited to blocked bloomfilters, and in other embodiments, the pruning index may be implementedusing other filters such as bloom filters, hash filters, or cuckoofilters.

FIGS. 8-12 are flow diagrams illustrating operations of the databasesystem 102 in performing a method 800 for generating and using a pruningindex in processing a database query, in accordance with someembodiments of the present disclosure. The method 800 may be embodied incomputer-readable instructions for execution by one or more hardwarecomponents (e.g., one or more processors) such that the operations ofthe method 800 may be performed by components of database system 102.Accordingly, the method 800 is described below, by way of example withreference thereto. However, it shall be appreciated that the method 800may be deployed on various other hardware configurations and is notintended to be limited to deployment within the database system 102.

Depending on the embodiment, an operation of the method 800 may berepeated in different ways or involve intervening operations not shown.Though the operations of the method 800 may be depicted and described ina certain order, the order in which the operations are performed mayvary among embodiments, including performing certain operations inparallel or performing sets of operations in separate processes. Forexample, although the use and generation of the pruning index aredescribed and illustrated together as part of the method 800, it shallbe appreciated that the use and generation of the pruning index may beperformed as separate processes, consistent with some embodiments.

At operation 805, the compute service manager 108 accesses a sourcetable that is organized into a plurality of micro-partitions. The sourcetable comprises a plurality of cells organized into rows and columns anda data value is included in each cell.

At operation 810, the compute service manager 108 generates a pruningindex based on the source table. The pruning index comprises a set offilters (e.g., a set of blocked bloom filters) that index distinctN-grams in each column of each micro-partition of the source table. Afilter is generated for each micro-partition in the source table andeach filter is decomposed into multiple numeric columns (e.g., 32numeric columns) to enable integer comparisons. Consistent with someembodiments, the pruning index comprises a plurality of rows and eachrow comprises at least a micro-partition identifier and a set of bloomfilters. Consistent with some embodiments, the compute service manager108 generates the pruning index in an offline process before receiving aquery. The compute service manager 108 stores the pruning index in adatabase with an association with the source table such that the pruningindex can be retrieved upon receiving a query directed at the sourcetable.

At operation 815, the compute service manager 108 receives a querydirected at the source table. The query can comprise an equalitypredicate (e.g., “=”), a pattern matching predicate (e.g., LIKE, ILIKE,CONTAINS, STARTSWITH, or ENDSWITH), or a regular expression predicate(e.g., RLIKE, REGEXP, or REGEXP_LIKE). In instances in which the queryincludes a pattern matching predicate, the query specifies a searchpattern for which matching stored data in the source table is to beidentified. In instances in which the query includes a regularexpression predicate, the query specifies a regular expression searchpattern comprising a sequence of characters to be matched againstsubject text (e.g., in a source table).

At operation 820, the compute service manager 108 accesses the pruningindex associated with the source table based on the query being directedat the source table. For example, the database 114 may store informationdescribing associations between tables and pruning indexes.

At operation 825, the compute service manager 108 uses the pruning indexto prune the set of micro-partitions of the source table to be scannedfor data that satisfies the query (e.g., a data value that satisfies theequality predicate or data that matches the search pattern). That is,the compute service manager 108 uses the pruning index to identify areduced scan set comprising only a subset of the micro-partitions of thesource table. The reduced scan set includes one or more micro-partitionsin which data that satisfies the query is potentially stored. The subsetof micro-partitions of the source table includes micro-partitionsdetermined to potentially include data that satisfies the query based onthe set of bloom filters in the pruning index.

At operation 830, the execution platform 110 processes the query. Inprocessing the query, the execution platform 110 scans the subset ofmicro-partitions of the reduced scan set while foregoing a scan of theremaining micro-partitions. In this way, the execution platform 110searches only micro-partitions where matching data is potentially storedwhile foregoing an expenditure of additional time and resources to alsosearch the remaining micro-partitions in which it is known, based on thepruning index, that matching data is not stored.

Consistent with some embodiments, rather than providing a reduced scanset with micro-partitions of the source table to scan for data, thecompute service manager 108 may instead identify and compile a set ofnon-matching micro-partitions. The compute service manager 108 or theexecution platform 110 may remove micro-partitions from the scan setbased on the set of non-matching micro-partitions.

As shown in FIG. 9 , the method 800 may, in some embodiments, furtherinclude operations 905 and 910. Consistent with these embodiments, theoperations 905 and 910 may be performed as part of the operation 810where the compute service manager 108 generates the pruning index. Theoperations 905 and 910 are described below in reference to a singlemicro-partition of the source table simply for ease of explanation.However, it shall be appreciated that in generating the pruning index,the compute service manager 108 generates a filter for eachmicro-partition of the source table and thus, the operations 905 and 910may be performed for each micro-partition of the source table.

At operation 905, the compute service manager 108 generates a filter fora micro-partition of the source table. For example, the compute servicemanager 108 may generate a blocked bloom filter for the micro-partitionthat indexes distinct N-grams in each column of the micro-partition ofthe source table. The filters are generated using a set of fingerprintsgenerated for each searchable data value in the micro-partition.

Consistent with some embodiments, for a given data value in themicro-partition, the compute service manager 108 can generate the set offingerprints based on a set of N-grams generated for the data value. Theset of N-grams can be generated based on the data value and/or one ormore preprocessed variants of the data value. The compute servicemanager 108 can generate a fingerprint based on a hash that is computedover an N-gram. In computing the hash, the compute service manager 108may utilize a rolling hash function or other known hashing scheme thatallows individual characters to be added or removed from a window ofcharacters. Each generated fingerprint is used to populate a cell in thefilter.

At operation 910, which is optional in some embodiments, the computeservice manager 108 merges one or more rows of the filter. The computeservice manager 108 can merge rows by performing a logical OR operation.The compute service manager 108 may merge rows of the filter until adensity threshold is reached, where the density refers to the ratio of1s and 0s in a row. The density threshold may be based on a target falsepositive rate.

As shown in FIG. 10 , the method 800 may, in some embodiments, includeoperations 1005, 1010, 1015, 1020, 1025, 1030, and 1035. Consistent withthese embodiments, the operations 1005, 1010, and 1015 may be performedprior to or as part of operation 810 where the compute service manager108 generates the pruning index for the source table. At operation 1005,the compute service manager 108 preprocesses the data values in thecells of the source table. In preprocessing a given data value, thecompute service manager 108 generates one or more preprocessed variantsof the data value. In performing the preprocessing, the compute servicemanager performs one or more normalization operations to a given datavalue. The compute service manager 108 can utilize one of several knownnormalization techniques to normalize data values (e.g., normalizationfrom canonical composition).

For a given data value, the preprocessing performed by the computeservice manager 108 can include, for example, any one or more of:generating a case-agnostic variant, (e.g., by converting uppercasecharacters to lowercase characters), generating one or more misspelledvariants based on common or acceptable misspellings of the data value,and generating one or more synonymous variants corresponding to synonymsof the data value. In general, in generating a preprocessed variant(e.g., case-agnostic variant, misspelled variant, a synonymous variant,or a variant with special characters to indicate a start and end to adata value), the compute service manager 108 uses a common knowledgebase to transform a data value into one or more permutations of theoriginal data value.

As an example of the forgoing, the string “Bob” can be transformed intothe case-agnostic variant “bob.” As another example, the preprocessedvariants of “bob,” “bbo,” and “obb” can be generated for the string“Bob” to account for misspellings.

At operation 1010, the compute service manager 108 generates a set ofN-grams for each preprocessed variant. An N-gram in this context refersto a contiguous sequence of N-items (e.g., characters or words) in agiven value. For a given preprocessed variant of a data value in thesource table, the compute service manager 108 transforms the value intomultiple segments of N-length. For example, for a string, the computeservice manager 108 can transform the string into multiple sub-stringsof N-characters.

Depending on the embodiment, the value of N can be predetermined ordynamically computed at the time of generating the pruning index. Inembodiments in which the value of N is precomputed, the compute servicemanager 108 can determine an optimal value for N based on a data type ofvalues in the source table.

In some embodiments, the set of N-grams can include N-grams of differentsizes for a given data value. That is, multiple values of N can be usedin generating the set of N-grams. For example, for a given data value,the set of N-grams can include a first N-gram that is a first size(e.g., an N-gram generated using a first value of N) and a second N-gramthat is a second size (e.g., an N-gram generated using a second value ofN).

At operation 1015, the compute service manager 108 generates a set offingerprints for each set of N-grams. The compute service manager 108can generate a fingerprint by computing a hash over an N-gram or aportion thereof. In computing the hash, the compute service manager 108may utilize a rolling hash function or other known hashing scheme thatallows individual characters to be added or removed from a window ofcharacters. An example hash function used by the compute service manager108 is the XxHash( ) function, although other known hash functions canbe utilized. Each generated fingerprint can be used to populate a cellin the filter.

Consistent with these embodiments, the operations 1020, 1025, and 1030can be performed prior to or as part of operation 820 where the computeservice manager 108 prunes the scan set using the pruning index. Atoperation 1020, the compute service manager 108 preprocesses a searchpattern included in the query. In preprocessing the search pattern, thecompute service manager 108 performs the same preprocessing operationsthat are performed on the data values in the source table at 1005 toensure that the characters of the search pattern fit the pruning index.Hence, in preprocessing the search pattern, the compute service manager108 can perform any one or more of: generating a case-agnostic variantof the search pattern (e.g., by converting uppercase characters tolowercase characters), generating one or more misspelled variants basedon common or acceptable misspellings of the search pattern, generatingone or more synonymous variants corresponding to synonyms of the searchpattern, and generating a variant that includes special characters tomark a start and end of the search pattern. In preprocessing a givenpattern, the compute service manager 108 can generate one or morepreprocessed variants of the search pattern. For example, the computeservice manager 108 can generate any one or more of: a case-agnosticvariant, misspelled variant, or a synonymous variant for the searchpattern. As a further example, the compute service manager 108 cangenerate a variant that includes special characters to indicate a startand end of a search pattern (e.g., “{circumflex over ( )}testvalue$” forthe search pattern “testvalue”).

At operation 1025, the compute service manager 108 generates a set ofN-grams for the search pattern based on the one or more preprocessedvariants of the search pattern. The compute service manager 108 uses thesame value for N that was used to generate the pruning index. Inembodiments in which the compute service manager 108 uses multiplevalues for N in generating the pruning index, the compute servicemanager 108 uses the same values for generating the set of N-grams forthe search pattern.

In an example, the query includes the following statement:

WHERE a ILIKE ‘%LoremIpsum%Dolor%Sit%Amet’

In this example, ‘%LoremIpsum%Dolor%Sit%Amet’ is the search pattern andin preprocessing the search pattern, the compute service manager 108converts the search pattern to all lower case to create a case-agnosticvariant: ‘%loremipsum%dolor%sit%amet’. The compute service manager 108splits the search pattern into segments at the wild card positions,which, in this example, produces the following sub-strings:“loremipsum,” “dolor,” “sit,” and “amet.” Based on these sub-strings,the compute service manager 108 generates the following set of N-grams:

Set [“lorem”, “oremi”, “remip”, “emips”, “mipsu”, “ipsum”, “dolor”]

In this example N is 5, and thus the compute service manager 108discards the sub-strings “sit” and “amet” as their length is less than5.

At operation 1030, the compute service manager 108 generates a set offingerprints based on each set of N-grams generated based on the searchpattern. As with the fingerprints generated based on the N-grams ofsearchable values from the source table, the compute service manager 108can generate a fingerprint for the search pattern by computing a hashover the N-gram of the searchable value, or a portion thereof.

As shown, consistent with these embodiments, the operation 1035 can beperformed as part of the operation 825 where the compute service manager108 prunes the scan set using the pruning index. At operation 1035, thecompute service manager 108 uses the set of N-grams generated based onthe search pattern to identify a subset of micro-portions of the sourcetable to scan based on the pruning index. The compute service manager108 may identify the subset of micro-partitions by generating a set offingerprints based on the set of N-grams (e.g., by computing a hash foreach N-gram), comparing the set of fingerprints to values included inthe pruning index (e.g., fingerprints of stored data values in thesource table), and identifying one or more values in the pruning indexthat match one or more fingerprints in the set of fingerprints generatedbased on the N-grams of the search pattern. Specifically, the computeservice manager 108 identifies one or more micro-partitions thatpotentially store data that satisfies the query based on fingerprints ofdata values in the pruning index that match fingerprints in the set offingerprints computed for the search pattern. That is, a fingerprint(e.g., hash value computed based on an N-gram of a preprocessed storeddata value in the source table) in the pruning index that matches afingerprint generated from an N-gram of the search pattern (e.g., a hashvalue computed based on the N-gram) indicates that matching data ispotentially stored in a corresponding column of the micro-partitionbecause the N-gram generated from the search pattern is stored in thecolumn of the micro-partition. The corresponding micro-partition can beidentified by the compute service manager 108 based on the matchingfingerprint in the pruning index.

Consistent with some embodiments, in identifying the subset ofmicro-partitions, the compute service manager 108 uses the pruning indexto identify any micro-partitions that contain any one of thefingerprints generated from the search pattern N-grams, and from thesemicro-partitions, the compute service manager 108 then identifies themicro-partitions that contain all the N-grams. That is, the computeservice manager 108 uses the pruning index to identify a subset ofmicro-partitions that contain data matching all fingerprints generatedbased on the N-grams of the search pattern. For example, givenfingerprints f1, f2, and f3, the compute service manager 108 uses thepruning index to determine: a first micro-partition and secondmicro-partition contain data corresponding to f1; the secondmicro-partition and a third micro-partition contains data correspondingto f2; and the first, second, and third micro-partitions contain datacorresponding to f3. In this example, the compute service manager 108selects only the second micro-partition for scanning based on the secondmicro-partition containing data that corresponds to all threefingerprints.

As shown in FIG. 11 , the method 800 may, in some embodiments, includeoperations 1105, 1110, 1115, 1120, 1125, 1130, 1135, 1140, and 1145.Consistent with these embodiments, the operations 1105, 1110, 1115, and1120 may be performed as part of operation 1010 where the computeservice manager 108 generates a set of N-grams for each preprocessedvariant of each data value in the source table. In some embodiments, theoperations 1105, 1110, 1115, and 1120 may also be performed as part ofoperation 1025 where the compute service manager 108 generates a set ofN-grams for each preprocessed variant of the search pattern.

At operation 1105, the compute service manager 108 generates a firstN-gram for a data value (e.g., a data value in the source table, apreprocessed variant of a data value in the source table, or a searchpattern included in a query) using a first value of N. Accordingly, thefirst N-gram is a first length. At operation 1110, the compute servicemanager 108 generates a second N-gram for the data value using a secondvalue of N. Hence, the second N-gram is a second length.

At operation 1115, the compute service manager 108 generates a thirdN-gram for the data value using a third value of N (an N-gram that is athird length). At operation 1120, the compute service manager 108generates a Mth N-gram for the data value using a Mth value of N (anN-gram that is an Mth length). Each N-gram in the set of N-gramsgenerated for the data value starts from the same offset, and thus, allN-grams in the set include the first N-gram as a prefix.

In an example of the foregoing operations, the first value of N is 5,the second value of N is 6, the third value of N is 7, the Mth value ofN is 8, and the data value is “testvalue.” In this example: the firstN-gram is “testy”; the second N-gram is “testva”; the third N-gram is“testval”; and the Mth N-gram is “testvalu.” Although consecutive valuesof N are described in this and other examples, it shall be appreciatedthat the prefix N-gram indexing techniques described herein are notlimited to consecutive values of N and in some embodiments,non-consecutive values of N can be used (e.g., N=5, 6, 7, 14).

Consistent with these embodiments, the operations 1125, 1130, 1135, 1140may be performed as part of operation 1015 where the compute servicemanager 108 generates a set of fingerprints based on each set ofN-grams. In some embodiments, the operations 1125, 1130, 1135, and 1140may be performed as part of operation 1030 where the compute servicemanager 108 generates a set of fingerprints for the search pattern.

At operation 1125, the compute service manager 108 generates a firstfingerprint based on the first N-gram. The compute service manager 108may generate the first fingerprint by computing a first hash over thefirst N-gram. In the “testvalue” example introduced above, the computeservice manager 108 generates the first fingerprint by computing a hashover “testy” (e.g., hash5=compute_hash (“testy”, 0)).

At operation 1130, the compute service manager 108 generates a secondfingerprint based on the second N-gram and the first fingerprint. Insome embodiments, the compute service manager 108 can generate thesecond fingerprint by computing a second hash over the second N-gram.Consistent with these embodiments, in the “testvalue” example fromabove, the compute service manager 108 can generate the secondfingerprint by computing a hash over “testva” (e.g., hash6=compute_hash(“testva”)). In some embodiments, the compute service manager 108 maygenerate the second fingerprint using the first hash as a seed for thehashing function used to compute a second hash over a portion of thesecond N-gram that excludes the first N-gram. Consistent with theseembodiments, in the “testvalue” example from above, the compute servicemanager 108 generates the second fingerprint by computing a hash over“a” using the hash of “testv” as the seed (e.g., hash6=compute_hash(“a”, hash5)).

At operation 1135, the compute service manager 108 generates a thirdfingerprint based on the third N-gram and the second fingerprint. Insome embodiments, the compute service manager 108 can generate the thirdfingerprint by computing a third hash over the third N-gram. Consistentwith these embodiments, in the “testvalue” example from above, thecompute service manager 108 can generate the third fingerprint bycomputing a hash over “testval” (e.g., hash7=compute_hash (“testval”)).In some embodiments, the compute service manager 108 may generate thethird fingerprint using the second hash as a seed for a hashing functionused to compute a third hash over a portion of the third N-gram thatexcludes the second N-gram. Consistent with these embodiments, in the“testvalue” example from above, the compute service manager 108generates the third fingerprint by computing a hash over “I” using thepreviously computed hash of “a” as the seed (e.g., hash7=compute_hash(“1”, hash6)).

At operation 1140, the compute service manager 108 generates a Mthfingerprint based on the Mth N-gram and a (M−1) fingerprint. In someembodiments, the compute service manager 108 can generate the Mthfingerprint by computing a Mth hash over the Mth N-gram. Consistent withthese embodiments, in the “testvalue” example from above, the computeservice manager 108 can generate the Mth fingerprint by computing a hashover “testvalu” (e.g., hash8=compute_hash (“testvalu”)). In someembodiments, the compute service manager 108 may generate the Mthfingerprint by using the M−1 hash as a seed for a hashing function usedto compute a Mth hash over a portion of the Mth N-gram that excludes theM−1 N-gram. Consistent with these embodiments, in the “testvalue”example, the computer service manager 108 generates the Mth fingerprintby computing a hash over “u” using the previously computed hash of “|”(e.g., hash8=compute_hash (“u”, hash7)).

Consistent with some embodiments, the operation 1145 can be performed aspart of operation 810 where the compute service manager 108 generatesthe pruning index. At operation 1145, the compute service manager 108determines, based on the first hash, a filter in the set of filters ofthe pruning index to populate using the first-Mth fingerprints. That is,fingerprints generated based on N-grams that share the first N-gram as aprefix are used to populate the same filter. In embodiments in which thepruning index comprises one or more blocked bloom filters, the computeservice manager 108 can use the first hash to determine a bloom filterblock to populate using the first-Mth fingerprints (e.g., by settingbits in the bloom filter block). By using the fingerprints to populatethe same bloom filter block in this manner, the compute service manager108 can maintain low lookup costs for the pruning index.

In some embodiments, the bits for a hierarchy can be spread overmultiple bloom filter rows to address deep hierarchies (e.g.,hierarchies comprising 10 or more levels). For example, assuming ahierarchy comprising 10 levels, bits corresponding to the first 5 levelsmay be placed in a first bloom filter row while bits corresponding tothe second 5 levels can be placed in a second bloom filter row.Populating bloom filters in this manner ensures that a bloom filter rowdoes not become overpopulated because of deep hierarchical data.

As shown in FIG. 12 , the method 800 may, in some embodiments, includeoperations 1205, 1210, and 1215. Consistent with these embodiments, theoperations may be performed prior to or as part of operation 810 wherethe compute service manager 108 generates a pruning index for a sourcetable. In some embodiments, the operations 1205 and 1210 may also beperformed as part of or prior to operation 825 where the compute servicemanager 108 prunes the scan set using the pruning index.

At operation 1205, the compute service manager 108 decomposes a dataitem into multiple segments. The sizes of the segments may be uniform ormay be varied. In embodiments in which the operation 1205 is performedprior to or as part of the operation 810 the data item may, for example,comprise a data value from a single column in the source table, acombination of two or more data values from different columns in thesource table, or a preprocessed variant thereof. In embodiments in whichthe operation 1205 is performed prior to or as part of the operation825, the data item may, for example, comprise a search pattern includedin the query or a preprocessed variant thereof.

In instances in which the data item comprises two or more data valuesfrom the source table, the two or more data values may have ahierarchical relationship. In a first example, the data item comprises“San Francisco, Calif.,” which corresponds to a combination of a Cityand a State, each of which may be stored in separate columns of thesource table. In a second example, the data item comprises the followinginternet protocol (IP) address: “192.168.1.40”. Generally, an IP addresscomprises a network identifier that identifies a network and a hostidentifier that identifies a device.

As shown, the operation 1205 can include operations 1206, 1207, and1208. At operation 1206, the compute service manager 108 determines aroot segment (e.g., a prefix) for the data item. In the first example,the compute service manager 108 may determine the State “California” isthe root segment of the data item. In the second example, the computeservice manager 108 may determine the network identifier “192.168.1” isthe root segment of the data item.

At operation 1207, the compute service manager 108 determines a firstchild segment of the data item and at operation 1208, the computeservice manager 108 determines an Mth child segment for the data item.Each of the child segments start from the same offset, and thus, thechild segments include the root segment as a prefix. It shall beappreciated that the number of child segments for the data itemdetermined by the compute service manager 108 depends on the type ofdata item, and in some instances, the number M of child segments may belimited to one.

In the first example discussed above, the compute service manager 108determines the city “San Francisco” is the first (and only) childsegment for the data item. In the second example discussed above, thecompute service manager 108 determines the host identifier “40” is thefirst (and only) child segment for the data item.

At operation 1210, the compute service manager 108 generates a set offingerprints for the data item based on the multiple components.Consistent with these embodiments, the operation 1210 can includeoperations 1211, 1212, and 1213. At operation 1211, the compute servicemanager 108 generates a first fingerprint based on the root segment ofthe data item. The compute service manager 108 may generate the firstfingerprint by computing a first hash over the root segment. In thefirst example discussed above, the compute service manager 108 generatesthe first fingerprint by computing a hash over “California” (e.g.,hashRoot=compute_hash (“California”, 0)). In the second examplediscussed above, the compute service manager 108 generates the firstfingerprint by computing a hash over “192.168.1” (e.g.,hashRoot=compute_hash (“192.168.1”, 0)).

At operation 1212, the compute service manager 108 generates a secondfingerprint based on the first child segment of the data item and thefirst fingerprint. The compute service manager 108 may generate thesecond fingerprint using the first hash as a seed for the hashingfunction used to compute a second hash over the first child segment ofthe data item. At operation 1213, the compute service manager 108generates a M+1 fingerprint for the data item based on the Mth childsegment and Mth fingerprint. The compute service manager 108 maygenerate the M+1 fingerprint by using the Mth hash as a seed for ahashing function used to compute a hash over the Mth child segment ofthe data item.

In the first example from above, the compute service manager 108generates the second fingerprint by computing a hash over “SanFrancisco” using the hash of “California” as the seed (e.g.,hashChild1=compute_hash (“San Francisco”, hashRoot)). In the secondexample from above, the compute service manager 108 generates the secondfingerprint by computing a hash over “40” using the hash of “192.168.1”as the seed (e.g., hashChild1=compute_hash (“40”, hashRoot)).

Consistent with some embodiments, the operation 1215 can be performed aspart of operation 810 where the compute service manager 108 generatesthe pruning index. At operation 1215, the compute service manager 108determines, based on the first hash, a filter in the set of filters ofthe pruning index to populate using the set of fingerprints generatedfor the data item. That is, fingerprints generated based on componentsof the data item that share the root segment as a prefix are used topopulate the same filter. In embodiments in which the pruning indexcomprises one or more blocked bloom filters, the compute service manager108 can use the first hash to determine a bloom filter block to populateusing the set of fingerprints (e.g., by setting bits in the bloom filterblock). Populating a given filter, a different number of bits can beused for the fingerprint of different levels in a hierarchy. Forexample, the number of bits used to populate the filter for eachfingerprint can be gradually decreased as the level for which thefingerprint was produced is increased. By using the fingerprints topopulate the same filter in this manner, the compute service manager 108can maintain low lookup costs for the pruning index.

As shown in FIG. 13 , the method 800 may, in some embodiments, includeoperations 1305, 1310, 1315, and 1320. The operations 1305, 1310, 1315,and 1320 can be performed in instances in which the query comprises aregular expression search pattern to be matched against the sourcetable. Consistent with these embodiments, the operations 1305, 1310, and1315 can be performed subsequent to the operation 815 where the querythat includes the regular expression search pattern is received. Aregular expression search pattern comprises a sequence of characters inwhich each character is either a literal character or a metacharacterwith special meaning. An example of a regular expression search patternis RLIKE(email, ‘\\w+@snowflake\\.com’), which matches any value in thecolumn “email” that has a local-part with alphanumeric characters plus“_” followed by ‘@snowflake.com’.

At operation 1305, the compute service manager 108 converts the regularexpression search pattern to a pruning index predicate comprising a setof substring literals extracted from the regular expression searchpattern. Each substring literal corresponds to a portion of a string ofliteral characters specified by the regular expression search pattern.

A pruning index predicate may, in some instances, correspond to orinclude a LIKE predicate (also referenced herein as an “LI”). Theregular expression search pattern may comprise one or moremetacharacters where each metacharacter corresponds to a specialmeaning. Examples of metacharacters along with their special meaning areprovided below in TABLE 1.

TABLE 1 METACHARACTER MEANING {circumflex over ( )} Match must start atthe beginning of a file or line . Match must start at the beginning of afile or line [ ] Matches any of the characters within the brackets[{circumflex over ( )}] Matches any character except those within thebrackets $ Match must end at the end of a file or line ( ) Asubexpression \n Matches the n-th marked expression * Matches thepreceding element zero or more times {m, n} Matches the precedingelement between m and n times {m} Matches the preceding element m times? Matches the preceding element zero or more times + Matches thepreceding element one or more times | Matches either the expressionbefore or after the operatorAccordingly, in converting the regular expression search pattern to thepruning index predicate, the compute service manager 108 may interpretone or more metacharacters in accordance with the meaning thereof. Forexample, the compute service manager 108 may interpret regularexpression metacharacters as specified in TABLE 2 presented below.

TABLE 2 Expression for Pruning Element Interpretation Regular Expressionindex predicate s1 Literal string s1 s1 {circumflex over ( )} Ignored{circumflex over ( )}s1 s1 . Splits expressions s1.s2 s1%s2 [ ] Mightcreate disjunctive s1[bc]s2 s1bs2 OR s1cs2 predicates (see below)[{circumflex over ( )}] Not supported, but splits s1[{circumflex over( )}abc]s2 s1%s2 expressions $ Ignored s1$ s1 ( ) Treated as (s1)s2 s1AND s2 subexpression. Creates a conjunctive predicate. \n Ignored (notsupported in RLIKE) * Not supported, but splits s1(e1)*s2 s1%s2expressions {m,n} Preceding element (s1){2,3} s1s1% included m times ands1(e1){0,2}s2 s1%s2 followed by split if s1{2,2} s1s1 n > m {m}Preceding element (s1){2} s1s1 included m times if (s1){0} % m >= T,otherwise replaced by split ? Not supported, but splits s1(e1)?s2 s1%s2expressions + Interpreted as (s1)+ s1% one and splits expressions |Creates a disjunctive s1|s2 s1 OR s2 predicate

For some embodiments, the compute service manager 108 may rewritebracket expressions (i.e., “[ ]”) and character class expressions as asequence of OR predicates. For example, [abc] can be rewritten as ‘a’ OR‘b’ OR ‘c’. The compute service manager 108 can process characterclasses with a bounded number of alternatives in a similar manner.Typically, single characters are not used to query the pruning index, sothis approach may be helpful when combined with the concatenation ofsubstrings of surrounding predicates, as will be discussed in furtherdetail below. If there are too many alternatives in a class, theoverhead of rewriting the expression with ‘OR’ predicates might be toolarge. Accordingly, in these instances, the compute service manager 108may remove the expression and replace it with a wildcard character(e.g., ‘%’). Generally, certain metacharacters may not be supported andthese non-supported characters are replaced by a wildcard character(e.g., ‘%’). For some embodiments, only metacharacters that indicatethat a subexpression should be present at least once are used forquerying the pruning index (with the exception of “|”), whilepositioning metacharacters may be ignored.

The result of converting the regular expression search pattern to thepruning index predicate may be a simple sequence of substrings (e.g.,separated by “%”) or a complex predicate composed of AND and ORpredicates if the regular expression search pattern comprises ‘|’ or ‘[]’ metacharacters. As an example of the latter, the regular expression‘str1(str2|str3)str4’ can be converted to LI(‘str1’) AND (LI(‘str2’) ORLI(‘str3’)) AND LI(‘str4’). Accordingly, in converting the regularexpression search pattern to the pruning index predicate, the computeservice manager 108 may, in some instances, convert the regularexpression search pattern to a Boolean expression of substring literals,which, for some embodiments, is represented by an expression treegenerated by the computer service manager 108 based on the regularexpression search pattern, as will be discussed in further detail below.Additional examples for converting the regular search pattern to apruning index predicate are provided below in reference to TABLE 3.

TABLE 3 Regular Expression Pruning index predicate RLIKE(c,‘str1.str2.*’) LI(c, ‘str1%str2%’) RLIKE(c, LI(c, ‘state=’)‘state=(California|Arizona),US’)  AND (LI(c, ’California’) OR LI(c,‘Arizona’))  AND (LI(c, ‘%,US’)  LI(c, ‘state=California,US’) OR  LI(c,‘state=Arizona,US’) RLIKE(c, ‘state=[A-Z]{2}’) LI(c, ‘state=%’) RLIKE(c,‘str1(AB|CD){2}’) LI(c, ‘str1ABAB’) OR LI(c, ‘str1CDCD’) RLIKE(c,‘San.*[fF]?rancisco’) LI(c, ‘San%rancisco’) RLIKE(c, ‘Optimi[sz]ation’)LI(c, ‘Optimisation’) OR LI(c, ‘Optimization’) RLIKE(c,‘(str1)*[abc](str2)*’) LI(c, ‘%’) RLIKE(c, ‘str1(str2|str3)*str4’)xLI(c, ‘str1%str4’) RLIKE(c,‘(str1|str2)(str3)*”) LI(c, ‘str1%’) OR LI(c,‘str2%’) RLIKE(c, ‘str1((str2)*|str3)+str4’) LI(c, ‘str1%str4’)

At operation 1310, the compute service manager 108 generates a set ofN-grams for the regular expression search pattern based on the set ofsubstring literals extracted from the regular expression search pattern.The compute service manager 108 may generate one or more N-grams foreach substring literal. That is, the set of N-grams may include at leastone N-gram generated from each substring literal in the set ofsub-string literals.

For some embodiments, prior to generating the set of N-grams, thecompute service manager 108 may preprocess the set of substring literalsto generate a set of preprocessed variants in the manner described abovewith reference to operation 1020 of FIG. 10 , and in generating the setof N-grams, the compute service manager 108 may generate one or moreN-grams for any one or more preprocessed variants of the set ofsubstring literals.

In generating the set of N-grams based on the substring literals, thecompute service manager 108 uses the same value for N that was used togenerate the pruning index. In embodiments in which the compute servicemanager 108 uses multiple values for N in generating the pruning index,the compute service manager 108 uses the same values for generating theset of N-grams for the search pattern.

At operation 1315, the compute service manager 108 generates a set offingerprints based on each set of N-grams generated for the regularexpression search pattern. As with the fingerprints generated based onthe N-grams of searchable values from the source table, the computeservice manager 108 can generate a fingerprint for the search pattern bycomputing a hash over the N-gram of a substring literal (or preprocessedvariant of the substring literal) or a portion thereof.

As shown, consistent with these embodiments, the operation 1320 can beperformed as part of the operation 825 where the compute service manager108 prunes the scan set using the pruning index. At operation 1320, thecompute service manager 108 uses the set of N-grams generated based onthe set of substring literals extracted from the regular expressionsearch pattern to identify a subset of micro-portions of the sourcetable to scan based on the pruning index. The compute service manager108 may identify the subset of micro-partitions by comparing the set offingerprints to values included in the pruning index (e.g., fingerprintsof stored data values in the source table), and identifying one or morevalues in the pruning index that match one or more fingerprints in theset of fingerprints generated based on the N-grams of the searchpattern. Specifically, the compute service manager 108 identifies one ormore micro-partitions that potentially store data that satisfies thequery based on fingerprints of data values in the pruning index thatmatch fingerprints in the set of fingerprints computed for the set ofsubstring literals extracted from the regular expression search pattern.The corresponding micro-partition can be identified by the computeservice manager 108 based on the matching fingerprint in the pruningindex.

As shown in FIG. 14 , the method 800 may, in some embodiments, includeoperations 1405, 1410, 1415, 1420, and 1425. Consistent with theseembodiments, the operations 1405, 1410, 1415, 1420, and 1425 can beperformed as part of the operation 1305 where the compute servicemanager 108 extracts the set of substring literals from the regularexpression search pattern.

At operation 1405, the compute service manager 108 generates anexpression tree based on the regular expression search pattern. Theexpression tree is a tree data structure that represents a Booleanexpression of substring literals corresponding to the regular expressionsearch pattern. The expression tree comprises a set of subpredicates,each of which corresponds to a substring literal extracted from theregular expression search pattern. Nodes of the expression tree mayrepresent any one or more of: an AND predicate, an OR predicate, and asubstring literal (a subpredicate). As an example, for the regularexpression pattern .*(str1){2,}(str2|str3|str4)(str5)+[a-z]*(str6)?.*the compute service manager 108 generates the following: str1str1 AND(str2 OR str3 OR str4) AND str5.

Initially, the expression tree may comprise a binary tree. The computeservice manager 108, at operation 1410, can enhance the expression treeby performing a flattening process in which the binary tree is convertedto an ordinal tree (e.g., a tree with arbitrary degree where childrenare ordered). In this way, the compute service manager 108 can relax theassociativity of AND and OR predicates and make them k-ary instead ofexclusively binary (where k>2). As an example, for a node n, theflattening includes, for each child c of the same type, making childrenof c direct children of n and removing c. In doing so, the computeservice manager 108 maintains the order of children nodes. In addition,the compute service manager 108 eliminates unary AND nodes. Ineliminating a unary AND node, its child node becomes a child node of itsparent node.

An example of the flattening process is illustrated in FIG. 15A. Withreference to FIG. 15A, an expression tree in the form of a binary tree1500 is shown. A flattening process 1505 is performed on the binary tree1500 and as a result an ordinal tree 1510 is generated.

With returned reference to FIG. 14 , to further enhance the expressiontree, the compute service manager 108 merges consecutive leaf childrenof AND nodes in the expression tree, at operation 1415. The merging ofconsecutive leaf children of AND nodes is equivalent to eliminatingsuperfluous parentheses in the regular expression. In an example, theexpression tree includes an AND node representing an AND predicate, andthe AND node includes a first child leaf node representing a firstsubstring literal and a second child leaf node representing a secondsubstring literal. In this example, the compute service manager 108merges the first child leaf node and the second child leaf node of theAND node into a single node that represents a combination of the firstsubstring literal and the second substring literal.

An example of merging consecutive leaf children of AND nodes in theexpression tree is illustrated by FIG. 15B. With reference to FIG. 15B,an initial expression tree 1520 is shown. At 1525, consecutive leafchildren of AND nodes in the expression tree 1520 are merged to producean enhanced expression tree 1530. In the enhanced expression tree 1530,the “A” and “B” nodes (connected to the “AND” node) are merged to createan “AB” node and the “F” and “G” nodes (also connected to the “AND”node) are merged to create an “FG” node.

With returned reference to FIG. 14 , if an OR node is a child of an ANDnode and has adjacent sibling nodes which are leaves, the strings fromthese leaves can be concatenated to the strings of all leaf children orthe OR node. Accordingly, at operation 1420, the compute service manager108 concatenates substrings of OR predicates with adjacent strings. Thatis, the compute service manage 108 may concatenate a first substringliteral of an OR predicate with an adjacent second substring literal.This corresponds to, for example, converting the expression ‘a(b|c)d’ to‘(abd|acd)’.

In order to maximize the number of n-grams that will be searched foreach substring of the OR predicate, the compute service manager 108concatenates as much as possible from the adjacent strings. However,concatenating too large of a string can result in many of the samen-grams being repeated among children of the OR node. If n_max is themaximum size of n-grams of the pruning index, then it follows thatconcatenating up to (n_max−1) characters of the adjacent stringsmaximizes the number of n-grams for each string of the OR predicatewhile not creating repeated n-grams (unless there are repeated prefixesor suffixes among OR children before concatenation).

In an example, a subexpression is ‘abcde(f|g|h)’ and the max length ofn-grams is 4. In this example, concatenating 3 characters results inab(cdef|cdeg|cdeh), and concatenating 4 characters results ina(bcdef|bcdeg|bcdeh). Adding ‘b’ to the substring results in the 4-gram‘bcde’ being repeated. On the other hand, removing a suffix from thepreceding string reduces the number of n-grams produced. Instead ofremoving substrings from the adjacent strings, the compute servicemanager 108 copies them while maintaining the original adjacent strings.For example, for ‘abcde(f|g|h)’, the compute service manager 108generates ‘abcde(cdef|cdeg|cdeh)’. If the removed substring is theentire string, the compute service manager 108 may also eliminate thenode, as it will not produce n-grams that are not already in the ORpredicate.

As previously noted, the concatenation techniques described above can becombined with the replacement of the [ ] metacharacter by OR predicates.For example, the expression ‘str1[ABC]str2’ can be converted toLI(‘str1Astr2’) OR LI(‘str1Bstr2’) OR LI(‘str1Cstr2’). Note that if nostrings are available to be concatenated either before or after the [ ]expression, the compute service manager 108 may instead remove thisexpression and replace it with a ‘%’.

An example of the concatenation techniques described above areillustrated by FIG. 15C. With reference to FIG. 15C, an expression tree1540 is shown. At 1545, substrings of OR predicates are concatenatedwith adjacent substrings to produce enhanced expression tree 1550. Inthe enhanced expression tree 1550, the substrings “C,” “D,” and “E” ofthe OR predicate are each concatenated with the adjacent substrings “B”and “F.”

With returned reference to FIG. 14 , at operation 1425, the computeservice manager 108 simplifies the expression tree by removing one ormore nodes from the expression tree that represent subpredicates (e.g.,substrings) that do not produce an N-gram. For example, non-supportedmetacharacters in the regular expression may be replaced by wildcardcharacters in the pruning index predicate, as noted above. Thereplacement of non-supported metacharacters by wildcards can result insubpredicates whose substrings consist of wildcards only. The computeservice manager 108 may remove nodes representing such subpredicates asthey cannot be used to filter any strings (there are no N-grams togenerate from such predicates). Moreover, if a removed subpredicate is achild of an OR predicate, the OR predicate evaluates to true and canalso be removed, possibly leading to further removals of ancestorsubpredicates. e. In addition, the compute service manager 108 mayremove a node based on a length of the corresponding substring failingto satisfy a length constraint. That is, the compute service manager 108may remove a node representing a substring that is too short.

FIG. 15D illustrates an example of simplifying an expression tree byremoving one or more nodes representing subpredicates that do notproduce an N-gram. With reference to FIG. 15D, an initial expressiontree 1555 is shown. At 1560, the leftmost OR node of the expression tree1555 is simplified by removing the “A” node given that the expression “AOR %” would not result in pruning of any partitions, which also resultsin remove of the ancestorial “OR” node to which the leftmost wildcard“%” node would otherwise be the only child node. A result of thesimplification performed at 1560 is shown in expression tree 1565. At1570, another simplification is performed on expression tree 1565 byremoving the “C” node from the expression tree 1565 given that theexpression “C OR %” would not result in pruning any partitions, whichalso results in removal of the ancestorial “OR” node to which theleftmost wildcard “%” node would otherwise be the only child node. Aresult of the simplification performed at 1570 is shown in expressiontree 1575. At 1580, yet another simplification is performed by removingthe rightmost wildcard “%” node, which also allows for the removal ofthe ancestorial “AND” node. A result of the simplification performed at1580 is shown by expression tree 1585. At operation 1590, yet anothersimplification is performed by removing the leftmost wildcard “%” nodeand its ancestorial “AND” node, a result of which is shown by enhancedexpression tree 1595.

It shall be noted that although the operations 1405, 1410, 1415, 1420,and 1425 are depicted and described in a certain order, the order inwhich the operations are performed may vary among embodiments, includingperforming certain operations in parallel or performing sets ofoperations in separate processes. Further, depending on the embodiments,any one or more of the operations 1405, 1410, 1415, 1420, and 1425 canbe repeated in different ways and one or intervening operations that arenot shown may also be performed. As an example, for some embodiments, asecond flattening operation can be performed subsequent to the mergingoperation, which contributes to some extra possibilities forconcatenating substrings with adjacent strings. That is, since themerging of adjacent nodes may result in AND nodes with a single child,an additional flattening operation can be performed to remove these ANDnodes. The additional flattening operation further simplifies theexpression tree and potentially allows for greater concatenation therebyproducing a greater number of N-grams.

An example of the foregoing is illustrated by FIG. 16 . With referenceto FIG. 16 , an initial expression tree 1600 is shown. The initialexpression tree 1600 corresponds to the following expression:(a|(b)(c(d)))e. A first flattening operation (e.g., operation 1410) isperformed on the expression tree 1600 at 1605, which results in theexpression tree 1610. A merging operation (e.g., operation 1415) isperformed on the expression tree 1610 at 1615. During the mergingoperation, the children of the bottom AND node are merged into a singlenode, as shown in the resulting expression tree 1620. A secondflattening operation is performed on the expression tree 1620 at 1625 toremove unary AND nodes, which results in the expression tree 1630. At1635, a concatenation operation (e.g., operation 1420) is performed onthe expression tree 1630 where the ‘E’ node is distributed to the ‘A’and ‘BCD’ nodes, a result of which is illustrated by expression tree1640.

Described implementations of the subject matter can include one or morefeatures, alone or in combination as illustrated below by way ofexample.

Example 1. A method comprising: receiving a query directed at a sourcetable organized into a set of batch units, the query comprising aregular expression search pattern; converting the regular expressionsearch pattern to a pruning index predicate comprising a set ofsubstring literals extracted from the regular expression search pattern;generating a set of N-grams based on the set of substring literalsextracted from the regular expression search pattern; accessing apruning index associated with the source table, the pruning indexindexing distinct N-grams in each column of the source table;identifying, using the pruning index, a subset of batch units to scanfor data matching the query based on the set of N-grams; and processingthe query by scanning the subset of batch units.

Example 2. The method of Example 1, wherein the converting of theregular expression search pattern to the pruning index predicatecomprises converting the regular expression search pattern to a Booleanexpression of substring literals.

Example 3. The method of any one or more of Examples 1 or 2, wherein theconverting of the regular expression search pattern to the Booleanexpression comprises interpreting a metacharacter in the regularexpression search pattern according to a specific meaning of themetacharacter.

Example 4. The method of any one or more of Examples 1-3, whereinconverting the regular expression search pattern comprises replacing ametacharacter with an OR predicate.

Example 5. The method of any of more of Examples 1-4, wherein theconverting the regular expression search pattern to the pruning indexpredicate comprises generating an expression tree representing a Booleanexpression of substring literals, the expression tree comprising a treedata structure that includes the set of substring literals.

Example 6. The method of any of more of Examples 1-5, wherein theexpression tree comprises a binary tree, and the method furthercomprises flattening the expression tree by converting the binary treeto an ordinal tree.

Example 7. The method of any of more of Examples 1-6, wherein: theexpression tree comprises an AND node representing an AND predicate, theAND node including a first child leaf node representing a firstsubstring literal and a second child leaf node representing a secondsubstring literal; and the method further comprises merging the firstchild leaf node and the second child leaf node of the AND node in asingle node comprising a combination of the first substring literal andthe second substring literal.

Example 8. The method of any of more of Examples 1-7, further comprisingconcatenating a first substring literal of an OR predicate with anadjacent second substring literal.

Example 9. The method of any of more of Examples 1-8, further comprisingremoving, from the expression tree, a node corresponding to a substringliteral that does not produce an N-gram.

Example 10. The method of any of more of Examples 1-9, furthercomprising generating one or more fingerprints based on the set ofN-grams, wherein identifying the subset of batch units to scan for datamatching the query comprises identifying one or more values in thepruning index that match the one or more fingerprints.

Example 11. A system comprising: one or more processors of a machine;and at least one memory storing instructions that, when executed by theone or more processors, cause the machine to perform operationsimplementing any one of example methods 1 to 10.

Example 12. A machine-readable storage device embodying instructionsthat, when executed by a machine, cause the machine to performoperations implementing any one of example methods 1 to 11.

FIG. 17 illustrates a diagrammatic representation of a machine 1700 inthe form of a computer system within which a set of instructions may beexecuted for causing the machine 1700 to perform any one or more of themethodologies discussed herein, according to an example embodiment.Specifically, FIG. 17 shows a diagrammatic representation of the machine1700 in the example form of a computer system, within which instructions1716 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1700 to perform any oneor more of the methodologies discussed herein may be executed. Forexample, the instructions 1716 may cause the machine 1700 to execute anyone or more operations of the method 800. As another example, theinstructions 1716 may cause the machine 1700 to implement portions ofthe functionality illustrated in any one or more of FIGS. 4-7 . In thisway, the instructions 1716 transform a general, non-programmed machineinto a particular machine 1700 (e.g., the compute service manager 108,the execution platform 110, and the data storage devices 206) that isspecially configured to carry out any one of the described andillustrated functions in the manner described herein.

In alternative embodiments, the machine 1700 operates as a standalonedevice or may be coupled (e.g., networked) to other machines. In anetworked deployment, the machine 1700 may operate in the capacity of aserver machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 1700 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a smart phone, a mobiledevice, a network router, a network switch, a network bridge, or anymachine capable of executing the instructions 1716, sequentially orotherwise, that specify actions to be taken by the machine 1700.Further, while only a single machine 1700 is illustrated, the term“machine” shall also be taken to include a collection of machines 1700that individually or jointly execute the instructions 1716 to performany one or more of the methodologies discussed herein.

The machine 1700 includes processors 1710, memory 1730, and input/output(I/O) components 1750 configured to communicate with each other such asvia a bus 1702. In an example embodiment, the processors 1710 (e.g., acentral processing unit (CPU), a reduced instruction set computing(RISC) processor, a complex instruction set computing (CISC) processor,a graphics processing unit (GPU), a digital signal processor (DSP), anapplication-specific integrated circuit (ASIC), a radio-frequencyintegrated circuit (RFIC), another processor, or any suitablecombination thereof) may include, for example, a processor 1714 and aprocessor 1712 that may execute the instructions 1716. The term“processor” is intended to include multi-core processors 1710 that maycomprise two or more independent processors (sometimes referred to as“cores”) that may execute instructions 1716 contemporaneously. AlthoughFIG. 17 shows multiple processors 1710, the machine 1700 may include asingle processor with a single core, a single processor with multiplecores (e.g., a multi-core processor), multiple processors with a singlecore, multiple processors with multiple cores, or any combinationthereof.

The memory 1730 may include a main memory 1732, a static memory 1734,and a storage unit 1736, all accessible to the processors 1710 such asvia the bus 1702. The main memory 1732, the static memory 1734, and thestorage unit 1736 store the instructions 1716 embodying any one or moreof the methodologies or functions described herein. The instructions1716 may also reside, completely or partially, within the main memory1732, within the static memory 1734, within the storage unit 1736,within at least one of the processors 1710 (e.g., within the processor'scache memory), or any suitable combination thereof, during executionthereof by the machine 1700.

The I/O components 1750 include components to receive input, provideoutput, produce output, transmit information, exchange information,capture measurements, and so on. The specific I/O components 1750 thatare included in a particular machine 1700 will depend on the type ofmachine. For example, portable machines such as mobile phones willlikely include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 1750 mayinclude many other components that are not shown in FIG. 17 . The I/Ocomponents 1750 are grouped according to functionality merely forsimplifying the following discussion and the grouping is in no waylimiting. In various example embodiments, the I/O components 1750 mayinclude output components 1752 and input components 1754. The outputcomponents 1752 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), other signal generators, and soforth. The input components 1754 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or another pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1750 may include communication components 1764operable to couple the machine 1700 to a network 1780 or devices 1770via a coupling 1782 and a coupling 1772, respectively. For example, thecommunication components 1764 may include a network interface componentor another suitable device to interface with the network 1780. Infurther examples, the communication components 1764 may include wiredcommunication components, wireless communication components, cellularcommunication components, and other communication components to providecommunication via other modalities. The devices 1770 may be anothermachine or any of a wide variety of peripheral devices (e.g., aperipheral device coupled via a universal serial bus (USB)). Forexample, as noted above, the machine 1700 may correspond to any one ofthe compute service manager 108, the execution platform 110, and thedevices 1770 may include the data storage device 206 or any othercomputing device described herein as being in communication with thenetwork-based database system 102 or the storage platform 104.

The various memories (e.g., 1730, 1732, 1734, and/or memory of theprocessor(s) 1710 and/or the storage unit 1736) may store one or moresets of instructions 1716 and data structures (e.g., software) embodyingor utilized by any one or more of the methodologies or functionsdescribed herein. These instructions 1716, when executed by theprocessor(s) 1710, cause various operations to implement the disclosedembodiments.

As used herein, the terms “machine-storage medium,” “device-storagemedium,” and “computer-storage medium” mean the same thing and may beused interchangeably in this disclosure. The terms refer to a single ormultiple storage devices and/or media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storeexecutable instructions and/or data. The terms shall accordingly betaken to include, but not be limited to, solid-state memories, andoptical and magnetic media, including memory internal or external toprocessors. Specific examples of machine-storage media, computer-storagemedia, and/or device-storage media include non-volatile memory,including by way of example semiconductor memory devices, e.g., erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), field-programmable gate arrays(FPGAs), and flash memory devices; magnetic disks such as internal harddisks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The terms “machine-storage media,” “computer-storage media,” and“device-storage media” specifically exclude carrier waves, modulateddata signals, and other such media, at least some of which are coveredunder the term “signal medium” discussed below.

In various example embodiments, one or more portions of the network 1780may be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local-area network (LAN), a wireless LAN (WLAN), awide-area network (WAN), a wireless WAN (WWAN), a metropolitan-areanetwork (MAN), the Internet, a portion of the Internet, a portion of thepublic switched telephone network (PSTN), a plain old telephone service(POTS) network, a cellular telephone network, a wireless network, aWi-Fi® network, another type of network, or a combination of two or moresuch networks. For example, the network 1780 or a portion of the network1780 may include a wireless or cellular network, and the coupling 1782may be a Code Division Multiple Access (CDMA) connection, a GlobalSystem for Mobile communications (GSM) connection, or another type ofcellular or wireless coupling. In this example, the coupling 1782 mayimplement any of a variety of types of data transfer technology, such asSingle Carrier Radio Transmission Technology (1×RTT), Evolution-DataOptimized (EVDO) technology, General Packet Radio Service (GPRS)technology, Enhanced Data rates for GSM Evolution (EDGE) technology,third Generation Partnership Project (3GPP) including 3G, fourthgeneration wireless (4G) networks, Universal Mobile TelecommunicationsSystem (UMTS), High-Speed Packet Access (HSPA), WorldwideInteroperability for Microwave Access (WiMAX), Long Term Evolution (LTE)standard, others defined by various standard-setting organizations,other long-range protocols, or other data transfer technology.

The instructions 1716 may be transmitted or received over the network1780 using a transmission medium via a network interface device (e.g., anetwork interface component included in the communication components1764) and utilizing any one of a number of well-known transfer protocols(e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions1716 may be transmitted or received using a transmission medium via thecoupling 1772 (e.g., a peer-to-peer coupling) to the devices 1770. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure. The terms “transmissionmedium” and “signal medium” shall be taken to include any intangiblemedium that is capable of storing, encoding, or carrying theinstructions 1716 for execution by the machine 1700, and include digitalor analog communications signals or other intangible media to facilitatecommunication of such software. Hence, the terms “transmission medium”and “signal medium” shall be taken to include any form of modulated datasignal, carrier wave, and so forth. The term “modulated data signal”means a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in the signal.

The terms “machine-readable medium,” “computer-readable medium,” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms are defined to includeboth machine-storage media and transmission media. Thus, the termsinclude both storage devices/media and carrier waves/modulated datasignals.

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Similarly, the methods described hereinmay be at least partially processor implemented. For example, at leastsome of the operations of the method 800 may be performed by one or moreprocessors. The performance of certain of the operations may bedistributed among the one or more processors, not only residing within asingle machine, but also deployed across a number of machines. In someexample embodiments, the processor or processors may be in a singlelocation (e.g., within a home environment, an office environment, or aserver farm), while in other embodiments the processors may bedistributed across a number of locations.

Although the embodiments of the present disclosure have been describedwith reference to specific example embodiments, it will be evident thatvarious modifications and changes may be made to these embodimentswithout departing from the broader scope of the inventive subjectmatter. Accordingly, the specification and drawings are to be regardedin an illustrative rather than a restrictive sense. The accompanyingdrawings that form a part hereof show, by way of illustration, and notof limitation, specific embodiments in which the subject matter may bepracticed. The embodiments illustrated are described in sufficientdetail to enable those skilled in the art to practice the teachingsdisclosed herein. Other embodiments may be used and derived therefrom,such that structural and logical substitutions and changes may be madewithout departing from the scope of this disclosure. This DetailedDescription, therefore, is not to be taken in a limiting sense, and thescope of various embodiments is defined only by the appended claims,along with the full range of equivalents to which such claims areentitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverall adaptations or variations of various embodiments. Combinations ofthe above embodiments, and other embodiments not specifically describedherein, will be apparent to those of skill in the art, upon reviewingthe above description.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In the appended claims, the terms “including” and“in which” are used as the plain-English equivalents of the respectiveterms “comprising” and “wherein.” Also, in the following claims, theterms “including” and “comprising” are open-ended; that is, a system,device, article, or process that includes elements in addition to thoselisted after such a term in a claim is still deemed to fall within thescope of that claim.

What is claimed is:
 1. A system comprising: at least one hardwareprocessor; and at least one memory storing instructions that cause theat least one hardware processor to perform operations comprising:receiving a query directed at a source table organized into a set ofbatch units, the query comprising a regular expression search pattern;converting the regular expression search pattern to a pruning indexpredicate, the converting of the regular expression search pattern tothe pruning index predicate comprising generating an expression treecomprising a tree data structure that includes a set of substringliterals extracted from the regular expression search pattern, theexpression tree comprising an AND node representing an AND predicate,the AND node including a first child leaf node representing a firstsubstring literal and a second child leaf node representing a secondsubstring literal, the converting of the regular expression searchpattern to the pruning index predicate further comprising merging thefirst child leaf node and the second child leaf node of the AND nodeinto a single node comprising a combination of the first substringliteral and the second substring literal; generating a set of N-gramsbased on the set of substring literals extracted from the regularexpression search pattern; accessing a pruning index associated with thesource table, the pruning index indexing distinct N-grams in each columnof the source table; identifying, using the pruning index, a subset ofbatch units to scan for data matching the query based on the set ofN-grams; and processing the query by scanning the subset of batch units.2. The system of claim 1, wherein the converting of the regularexpression search pattern further comprises interpreting a metacharacterin the regular expression search pattern according to a specific meaningof the metacharacter.
 3. The system of claim 1, wherein converting theregular expression search pattern further comprises replacing ametacharacter with an OR predicate.
 4. The system of claim 1, wherein:the expression tree comprises a binary tree; and the operations furthercomprising flattening the expression tree by converting the binary treeto an ordinal tree.
 5. The system of claim 1, wherein the operationsfurther comprise concatenating a first substring literal of an ORpredicate with an adjacent second substring literal.
 6. The system ofclaim 1, wherein the operations further comprise removing, from theexpression tree, a node corresponding to a substring literal that doesnot produce an N-gram.
 7. The system of claim 1, wherein the operationsfurther comprise generating one or more fingerprints based on the set ofN-grams, wherein identifying the subset of batch units to scan for datamatching the query comprises identifying one or more values in thepruning index that match the one or more fingerprints.
 8. A methodcomprising: receiving a query directed at a source table organized intoa set of batch units, the query comprising a regular expression searchpattern; converting the regular expression search pattern to a pruningindex predicate, the converting of the regular expression search patternto the pruning index predicate comprising generating an expression treecomprising a tree data structure that includes a set of substringliterals extracted from the regular expression search pattern, theexpression tree comprising an AND node representing an AND predicate,the AND node including a first child leaf node representing a firstsubstring literal and a second child leaf node representing a secondsubstring literal, the converting of the regular expression searchpattern to the pruning index predicate further comprising merging thefirst child leaf node and the second child leaf node of the AND nodeinto a single node comprising a combination of the first substringliteral and the second substring literal; generating a set of N-gramsbased on the set of substring literals extracted from the regularexpression search pattern; accessing a pruning index associated with thesource table, the pruning index indexing distinct N-grams in each columnof the source table; identifying, using the pruning index, a subset ofbatch units to scan for data matching the query based on the set ofN-grams; and processing the query by scanning the subset of batch units.9. The method of claim 8, wherein the converting of the regularexpression search pattern further comprises interpreting a metacharacterin the regular expression search pattern according to a specific meaningof the metacharacter.
 10. The method of claim 8, wherein converting theregular expression search pattern further comprises replacing ametacharacter with an OR predicate.
 11. The method of claim 8, wherein:the expression tree comprises a binary tree; and the method furthercomprises flattening the expression tree by converting the binary treeto an ordinal tree.
 12. The method of claim 8, further comprisingconcatenating a first substring literal of an OR predicate with anadjacent second substring literal.
 13. The method of claim 8, furthercomprising removing, from the expression tree, a node corresponding to asubstring literal that does not produce an N-gram.
 14. The method ofclaim 8, further comprising generating one or more fingerprints based onthe set of N-grams, wherein identifying the subset of batch units toscan for data matching the query comprises identifying one or morevalues in the pruning index that match the one or more fingerprints. 15.A computer-storage medium comprising instructions that, when executed byone or more processors of a machine, configure the machine to performoperations comprising: receiving a query directed at a source tableorganized into a set of batch units, the query comprising a regularexpression search pattern; converting the regular expression searchpattern to a pruning index predicate, the converting of the regularexpression search pattern to the pruning index predicate comprisinggenerating an expression tree comprising a tree data structure thatincludes a set of substring literals extracted from the regularexpression search pattern, the expression tree comprising an AND noderepresenting an AND predicate, the AND node including a first child leafnode representing a first substring literal and a second child leaf noderepresenting a second substring literal, the converting of the regularexpression search pattern to the pruning index predicate furthercomprising merging the first child leaf node and the second child leafnode of the AND node into a single node comprising a combination of thefirst substring literal and the second substring literal; generating aset of N-grams based on the set of substring literals extracted from theregular expression search pattern; accessing a pruning index associatedwith the source table, the pruning index indexing distinct N-grams ineach column of the source table; identifying, using the pruning index, asubset of batch units to scan for data matching the query based on theset of N-grams; and processing the query by scanning the subset of batchunits.
 16. The computer-storage medium of claim 15, wherein: theconverting of the regular expression search pattern further comprisesinterpreting a metacharacter in the regular expression search patternaccording to a specific meaning of the metacharacter.
 17. Thecomputer-storage medium of claim 15, wherein converting the regularexpression search pattern comprises replacing a metacharacter with an ORpredicate.
 18. The computer-storage medium of claim 15, wherein: theexpression tree comprises a binary tree; and the operations furthercomprise flattening the expression tree by converting the binary tree toan ordinal tree.
 19. The computer-storage medium of claim 15, whereinthe operations further comprise concatenating a first sub string literalof an OR predicate with an adjacent second substring literal.
 20. Thecomputer-storage medium of claim 15, wherein the operations furthercomprise removing, from the expression tree, a node corresponding to asub string literal that does not produce an N-gram.
 21. Thecomputer-storage medium of claim 15, wherein the operations furthercomprise generating one or more fingerprints based on the set ofN-grams, wherein identifying the subset of batch units to scan for datamatching the query comprises identifying one or more values in thepruning index that match the one or more fingerprints.